Proceedings ArticleDOI
Speech recognition using MFCC and DTW
Bhadragiri Jagan Mohan,N. Ramesh Babu +1 more
- pp 1-4
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TLDR
In this paper, an implementation of speech recognition system in MATLAB environment is explained, where two algorithms, Mel-Frequency Cepstral Coefficients (MFCC) and Dynamic Time Wrapping (DTW) are adapted for feature extraction and pattern matching respectively.Abstract:
Speech recognition has wide range of applications in security systems, healthcare, telephony military, and equipment designed for handicapped. Speech is continuous varying signal. So, proper digital processing algorithm has to be selected for automatic speech recognition system. To obtain required information from the speech sample, features have to be extracted from it. For recognition purpose the feature are analyzed to make decisions. In this paper implementation of Speech recognition system in MATLAB environment is explained. Mel-Frequency Cepstral Coefficients (MFCC) and Dynamic Time Wrapping (DTW) are two algorithms adapted for feature extraction and pattern matching respectively. Results are obtained by one time training and continuous testing phases.read more
Citations
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Proceedings ArticleDOI
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TL;DR: This paper demonstrates another scale of auditory frequency spectrum namely, Bark and Equivalent Rectangular Bandwidth (ERB) scales, which have achieved the better performance than the Mel scale.
Journal ArticleDOI
Voice-Print Recognition System Using Python And Machine Learning With IBM Watson
TL;DR: This system is going to use some machine learning concept SVM (Support Vector Machine), the SVC will be useful to differentiate the dataset and find out the actual required result so that user can get authentication to access the machine.
Book ChapterDOI
Evaluating the Effectiveness of Inhaler Use Among COPD Patients via Recording and Processing Cough and Breath Sounds from Smartphones
TL;DR: In this article, a machine learning algorithm operating on Mel-frequency Cepstral Coefficients of patients' cough and breath sounds was proposed to detect the effectiveness of inhaler usage.
Journal ArticleDOI
Speech signal analysis of alzheimer's diseases in farsi using auditory model system.
Maryam Momeni,Mahdiyeh Rahmani +1 more
TL;DR: Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD, demonstrating the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer's only using the few extracted features of the speech signal.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Posted Content
Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques
TL;DR: This paper presents the viability of MFCC to extract features and DTW to compare the test patterns and explains why the alignment is important to produce the better performance.
Speaker identification using mel frequency cepstral coefficients
Rashidul Hasan,Saifur Rahman +1 more
TL;DR: This paper presents a security system based on speaker identification based onMel frequency Cepstral Coefficients{MFCCs} have been used for feature extraction and vector quantization technique is used to minimize the amount of data to be handled.
Voice command recognition system based on mfcc and dtw
TL;DR: The feasibility of MFCC to extract features and DTW to compare the test patterns is presented and the non linear sequence alignment known as Dynamic Time Warping introduced by Sakoe Chiba has been used as features matching techniques.
Recognition of Isolated Words using Features based on LPC, MFCC, ZCR and STE, with Neural Network Classifiers
Bishnu Prasad Das,Ranjan Parekh +1 more
TL;DR: An accuracy of 85% is obtained by the combination of features, when the proposed approach is tested using a dataset of 280 speech samples, which is more than those obtained by using the features singly.